Knowledge base question answering (KBQA) aims to answer a question over a
knowledge base (KB). Early studies mainly focused on answering simple questions
over KBs and achieved great success. However, their performance on complex
questions is still far from satisfactory. Therefore, in recent years,
researchers propose a large number of novel methods, which looked into the
challenges of answering complex questions. In this survey, we review recent
advances on KBQA with the focus on solving complex questions, which usually
contain multiple subjects, express compound relations, or involve numerical
operations. In detail, we begin with introducing the complex KBQA task and
relevant background. Then, we describe benchmark datasets for complex KBQA task
and introduce the construction process of these datasets. Next, we present two
mainstream categories of methods for complex KBQA, namely semantic
parsing-based (SP-based) methods and information retrieval-based (IR-based)
methods. Specifically, we illustrate their procedures with flow designs and
discuss their major differences and similarities. After that, we summarize the
challenges that these two categories of methods encounter when answering
complex questions, and explicate advanced solutions and techniques used in
existing work. Finally, we conclude and discuss several promising directions
related to complex KBQA for future research.

本文综述了近年来解决复杂问题的知识库问答的最新进展，介绍了语义分析和信息检索方法，总结了这两类方法在回答复杂问题时遇到的挑战，并详细解释了现有工作中使用的先进解决方案和技术，最后探讨了与复杂 KBQA 相关的若干有前途的研究方向。

复杂知识库问答：综述

Complex Knowledge Base Question Answering: A Survey

Knowledge base question answering (KBQA) aims to answer a question over a
knowledge base (KB). Recently, a large number of studies focus on semantically
or syntactically complicated questions. In this paper, we elaborately summarize
the typical challenges and solutions for complex KBQA. We begin with
introducing the background about the KBQA task. Next, we present the two
mainstream categories of methods for complex KBQA, namely semantic
parsing-based (SP-based) methods and information retrieval-based (IR-based)
methods. We then review the advanced methods comprehensively from the
perspective of the two categories. Specifically, we explicate their solutions
to the typical challenges. Finally, we conclude and discuss some promising
directions for future research.

本论文总结了解决复杂知识库问答任务所面临的挑战及其解决方案，介绍了语义解析方法和信息检索方法两种主流方法，并从两种方法的角度全面地回顾了高级方法及其解决方案，最后总结并讨论了未来研究的一些有前途的方向。